Data assimilation of simulated SSS SMOS products in an ocean forecasting system

Sea Surface Salinity (SSS) measured from the future Soil Moisture and Ocean Salinity (SMOS) satellite has to be considered as a new type of data. This paper addresses the impact of assimilating two different simulated SSS data types (raw track observations versus a gridded processed product) in an ocean forecasting system through an Observing System Simulation Experiment (OSSE). The OSSE consists of hindcast experiments assimilating an operational dataset of Sea Surface Temperature (SST), in-situ profiles of temperature and salinity and Sea Level Anomalies (SLA) plus various simulated SMOS SSS data. These assimilation experiments use an eddy permitting model (1/3 degrees) covering the North Atlantic from 20 degrees S to 70 degrees N and a multivarlate assimilation system referred to as SAM2vl. This assimilation scheme is a Reduced Order Kalman Filter using a 3D multivariate modal decomposition of the forecast error covariance. The OSSE enables an illustration of the impact of SSS assimilation on a Mercator Ocean regional forecasting system. Several conclusions can be highlighted, such as the importance of the consistency of space/time scales between the data products and the ocean prediction systems used. This study should be viewed as a preliminary step for assimilation of SSS measured from space in an ocean forecasting system